Analyzing Retweet Dynamics of German MPs in the 19th Legislative Period

Assignment 2: Data Analysis Exercise

Author

Lukas Warode

In a data analysis exercise a real-world, but pre-defined and cleaned data set, is assigned from a small collection of examples. A list of descriptive measures, as well as community detection methods should be applied, using a programming language and libraries of choice (which have been introduced in the course before). In a short report, either including the code (e.g., a python notebook) or a text document with plots (in this case the code should be submitted additionally), the results should be presented and their meaning interpreted and discussed with respect to the dataset at hand. Students will have two weeks to solve this task individually at home. Grading will be one half the correct application of the descriptive measures and the other half the presentation (i.e., plots), the interpretation and the discussion of those results.

library(tidyverse)
library(igraph)
library(tidygraph)
library(ggraph)

Load and Manipulate Dataset

In my Master’s Thesis, I scale German MPs (MdBs) of the 19th legislative period across ideological dimensions that are determined in an unsupervised way by using parliamentary speeches and Tweets. While retweet dynamics do not play a big role in the scaling algorithm per se, party affiliation does and is particularly interesting for network analytical approaches, especially in the realm of clustering.

tweet_df_final <- readRDS("data/tweet_df_final_2.rds")

# further meta information 
od_tw_info <- read_csv("data/od_tw_merge_final.csv")

How many Tweets are there in total? Over 1.3 Million - quite a lot.

tweet_df_final %>% 
  nrow()

[1] 1301427

How many Twitter users are there in total across all MPs of the 19th legislative period?

tweet_df_final %>% 
  select(author_id, tw_id) %>% 
  distinct() %>% 
  nrow()

[1] 515

Create `data.frame` that exhibits information of referenced `Tweets`

retweet_df <- tweet_df_final %>% 
  select(referenced_tweets, tweet_type_character, tweet_type_final, id) %>% 
  mutate(
    # type of referenced tweet
    ref_tweet_types = map(1:length(referenced_tweets),
                          ~ referenced_tweets[[.x]][["type"]]),
    # id of referenced tweet (NOT id of user)
    ref_tweet_id = map(1:length(referenced_tweets),
                       ~ referenced_tweets[[.x]][["id"]])
  )

Get author information of `Tweets` that were referenced

author_id_ref_tweet <- retweet_df %>% 
  filter(ref_tweet_types == "retweeted") %>% 
  mutate(ref_tweet_id = unlist(ref_tweet_id)) %>% 
  left_join(
    tweet_df_final %>% select(id, author_id, tw_id), 
    by = c("ref_tweet_id" = "id")
  ) %>% 
  filter(!is.na(author_id), !is.na(tw_id)) %>% 
  select(id, ref_tweet_id, author_id, author_id, tw_id)

Create final `data.frame` that only consists of `retweets` that occured within the dataset

Filter for retweets and merge author information from retweeted Tweets.

# vector with IDs from Tweets that are from authors within the dataset
tweet_ids <- tweet_df_final$id

retweet_within_df <- retweet_df %>% 
  filter(ref_tweet_types == "retweeted") %>% 
  filter(ref_tweet_id %in% tweet_ids) %>% 
  left_join(tweet_df_final %>% select(id, author_id, tw_id)) %>% 
  # directedness: from (orginal tweet) -> to (retweeted tweet) 
  left_join(author_id_ref_tweet, by = "id", suffix = c("_to", "_from"))

How many Tweets are left after constructing the “within-retweet data.frame”? Just about 100000 or 7.7% of all Tweets.

retweet_within_df %>% nrow()

[1] 100311

paste0((nrow(retweet_within_df) / nrow(tweet_df_final) * 100) %>% round(1), "%")

[1] "7.7%"

Validate Retweet-Network Structure

retweet_within_df %>% select(tw_id_from, tw_id_to) %>% head(5)

      tw_id_from       tw_id_to
1   katjakipping dorisachelwilm
2    victorperli dorisachelwilm
3  niemamovassat dorisachelwilm
4 michel_brandt_ dorisachelwilm
5 dietmarbartsch dorisachelwilm

This makes actually sense! Doris Achelwilm (MP from Left Party) retweets Katja Kipping and Dietmar Bartsch, which do both play a central role in the Left Party: They were party leaders in the 19th legislative period.

Plot most `retweeted` MPs

First, let us create a vector with party colors.

party_colors <- c("#138BD8", "#000000", "#FFEE0A", "#529222", "#AE1862", "#E30019")
names(party_colors) <- c("AfD", "CDU/CSU", "FDP", "Grüne", "DIE LINKE.", "SPD")

We also create a ggplot2 theme, which is based on theme_bw(), but has some refinements.

theme_custom <- function() {
  font <- "Corbel"
  # base theme
  theme_bw() %+replace%
    theme(
      text = element_text(family = font),
      legend.text = element_text(size = 9),
      legend.title = element_blank(),
      legend.box.background = element_rect(
        colour = "black",
        fill = "white",
        linetype = "solid"
      ),
      # grid lines
      panel.grid.major = element_line(color = "grey60", size = 0.2),
      panel.grid.minor = element_line(color = "grey80", size = 0.1),
      # faceting
      strip.background = element_blank(),
      strip.text = element_text(color = "black"),
    )
}

Now, we set the new theme as default.

theme_set(theme_custom())

We also create a data.frame that just includes the party affiliation per MP.

party_user <- tweet_df_final %>% 
  distinct(tw_id, party)

Plot Top 20

Now, we count how often per MP a Tweet was retweeted by another MP. We plot the to 20 MPs in total and visualize the party affiliation.

retweet_within_df %>% 
  group_by(tw_id_from) %>%
  count() %>%
  ungroup() %>% 
  arrange(desc(n)) %>% 
  top_n(20) %>% 
  left_join(party_user, by = c("tw_id_from" = "tw_id")) %>%
  ggplot(aes(x = n, y = reorder(tw_id_from, n), fill = party)) + 
  geom_col() +
  scale_fill_manual(values = party_colors) +
  labs(y = "", title = "Most Prominent MPs by Retweets of other MPs") +
  theme_custom()

Interestingly, the AfD and FDP dominate the chart, while there is no single SPD MP within the top 20 most retweeted MPs within. Let’s check the pattern and plot by party! Overall, the results seem to make sense, given that party leaders and influential MPs are the most retweeted MPs according to the bar plot.

Plot Top 20 by Party

Now, let’s turn the attention to the most retweeted MPs by party. Both CDU/CSU and SPD, the biggest parties and also only members of the governing coalition of the 19th legislative period do not have as many influential MPs, measured by number of retweets across their colleagues, as opposition parties have.

retweet_within_df %>% 
  left_join(party_user, by = c("tw_id_from" = "tw_id")) %>%
  filter(!party %in% c("Fraktionslos", NA)) %>% 
  group_by(tw_id_from, party) %>%
  count() %>%
  group_by(party) %>% 
  arrange(desc(n)) %>%
  top_n(10) %>% 
  ggplot(aes(x = n, y = reorder(tw_id_from, n), fill = party)) + 
  geom_col() +
  facet_wrap(~ party, scales = "free_y", ncol = 2) +
  scale_fill_manual(values = party_colors) +
  labs(y = "", title = "Most Prominent MPs by Retweets of other MPs", subtitle = "Grouped by Party") +
  theme_custom()

Construct Retweet-Network

In a Retweet-Network each retweet is represented as a directed link in a network that connects from the original Tweet to the referenced retweet. Users that are often retweeted within the network are expected to be associated with some degree of (digital) political relevance, even though there a lot of potential confounding variables (e.g. number of Tweets, number of Followers, general social media behaviour and association with socio-demographic factors such as cohort affiliation).

retweet_network <- retweet_within_df %>% 
  select(from = tw_id_from, to = tw_id_to) %>% 
  as_tbl_graph(directed = TRUE) %>% 
  left_join(party_user, by = c("name" = "tw_id")) 
 
# get corresponding row ID (from ID) 
row_id_from <- retweet_network %>% 
  as_tibble() %>%
  mutate(id_from = row_number())

# update retweet network with edge information: name and party of retweeted users (from)
retweet_network <- retweet_network %>% 
  activate(edges) %>% 
  left_join(row_id_from, by = c("from" = "id_from")) %>% 
  rename(party_from = party, name_from = name) %>% 
  activate(nodes)

retweet_network

# A tbl_graph: 487 nodes and 100311 edges
#
# A directed multigraph with 1 component
#
# Node Data: 487 × 2 (active)
  name           party     
  <chr>          <chr>     
1 katjakipping   DIE LINKE.
2 victorperli    DIE LINKE.
3 niemamovassat  DIE LINKE.
4 michel_brandt_ DIE LINKE.
5 dietmarbartsch DIE LINKE.
6 susanneferschl DIE LINKE.
# … with 481 more rows
#
# Edge Data: 100,311 × 4
   from    to name_from     party_from
  <int> <int> <chr>         <chr>     
1     1    44 katjakipping  DIE LINKE.
2     2    44 victorperli   DIE LINKE.
3     3    44 niemamovassat DIE LINKE.
# … with 100,308 more rows

Degree Centrality

Out-degree

In the previous plots, we already calculated the out-degree centrality (which is the same as the number of outgoing edges per node or the number of original Tweets that have been retweeted within). Nevertheless, double-checking is always a good practice!

# %N>% activate nodes (tidygraph syntax)
retweet_network %N>%
  mutate(
    # in_degree = centrality_degree(mode = "in"),
    out_degree = centrality_degree(mode = "out")
  ) %>% 
  arrange(desc(out_degree)) %>% 
  select(name, out_degree, party) %>% 
  as_tibble() %>% 
  head(20) %>% 
  knitr::kable()

name	out_degree	party
c_lindner	4066	FDP
udohemmelgarn	3983	AfD
alice_weidel	3853	AfD
frank_pasemann	3794	AfD
brihasselmann	2475	Grüne
marcobuschmann	2294	FDP
schneider_afd	1830	AfD
beatrix_vstorch	1554	AfD
gtzfrmming	1330	AfD
dietmarbartsch	1208	DIE LINKE.
m_reichardt_afd	1164	AfD
konstantinkuhle	1162	FDP
olliluksic	1154	FDP
paulziemiak	1022	CDU/CSU
joanacotar	1021	AfD
konstantinnotz	956	Grüne
renner_afd	933	AfD
nicole_hoechst	921	AfD
jensspahn	901	CDU/CSU
johannesvogel	888	FDP

Now, let’s plot the distribution of the out-degree. There are a few outliers at the upper end.

retweet_network %N>%
  mutate(
    # in_degree = centrality_degree(mode = "in"),
    out_degree = centrality_degree(mode = "out")
  ) %>% 
  as_tibble() %>% 
  ggplot(aes(x = out_degree)) + 
  geom_histogram() +
  labs(x = "Out-Degree") +
  theme_custom()

The out-degree distribution is quite comparable across parties: Most MPs were never retweeted, but have important retweet hubs. However, for the Green and Left Party, the distribution seems to be a bit less skewed.

retweet_network %N>%
  filter(!party %in% c("Fraktionslos", NA)) %>% 
  mutate(
    # in_degree = centrality_degree(mode = "in"),
    out_degree = centrality_degree(mode = "out")
  ) %>% 
  as_tibble() %>% 
  ggplot(aes(x = out_degree, fill = party)) + 
  geom_histogram() +
  labs(x = "Out-Degree") +
  facet_wrap(~ party, scales = "free") +
  scale_fill_manual(values = party_colors) +
  theme_custom()

In-degree

The in-degree corresponds to the “inverse retweet behaviour”: It indicates which MPs retweeted Tweets of other MPs.

# %N>% activate nodes (tidygraph syntax)
retweet_network %N>%
  mutate(
    in_degree = centrality_degree(mode = "in"),
    # out_degree = centrality_degree(mode = "out")
  ) %>% 
  arrange(desc(in_degree)) %>% 
  select(name, in_degree, party) %>% 
  as_tibble() %>% 
  head(20) %>% 
  knitr::kable()

name	in_degree	party
udohemmelgarn	6002	AfD
schneider_afd	5001	AfD
frank_pasemann	3583	AfD
martinrosemann	3409	SPD
renner_afd	3270	AfD
olliluksic	2140	FDP
nicole_hoechst	1735	AfD
drandreasnick	1567	CDU/CSU
stbrandner	1519	AfD
rkiesewetter	1505	CDU/CSU
reinholdmdb	1494	FDP
c_jung77	1284	FDP
k_sa	1225	Grüne
elsnervongronow	1187	AfD
tschipanski	1006	CDU/CSU
ullinissen	991	SPD
andi_wagner	969	DIE LINKE.
gtzfrmming	868	AfD
djanecek	834	Grüne
steinekecdu	807	CDU/CSU

retweet_network %N>%
  mutate(
    in_degree = centrality_degree(mode = "in"),
    # out_degree = centrality_degree(mode = "out")
  ) %>% 
  as_tibble() %>% 
  ggplot(aes(x = in_degree)) + 
  geom_histogram() +
  labs(x = "In-Degree") +
  theme_custom()

retweet_network %N>%
  filter(!party %in% c("Fraktionslos", NA)) %>% 
  mutate(
    in_degree = centrality_degree(mode = "in"),
    # out_degree = centrality_degree(mode = "out")
  ) %>% 
  as_tibble() %>% 
  ggplot(aes(x = in_degree, fill = party)) + 
  geom_histogram() +
  labs(x = "In-Degree") +
  facet_wrap(~ party, scales = "free") +
  scale_fill_manual(values = party_colors) +
  theme_custom()

The in-degree distribution seems to be a bit more normally distributed, meaning that “inverse hubs”, or MPs that heavily retweeted other MPs, are also present but not as dominant as retweeted MP hubs. However, the AfD seems to dominate the retweet dynamic from this perspective, as the table above shows.

Comparing Out- and In-degree

In- and out-degree seem to be correlated (\(R = 0.53\)), however, there are outliers that influence the relationship heavily.

# %N>% activate nodes (tidygraph syntax)
retweet_network %N>%
  mutate(
    in_degree = centrality_degree(mode = "in"),
    out_degree = centrality_degree(mode = "out")
  ) %>% 
  select(name, in_degree, out_degree) %>% 
  as_tibble() %>% 
  left_join(party_user, by = c("name" = "tw_id")) %>%
  ggplot(aes(out_degree, in_degree)) + 
  geom_point(alpha = 0.5) +
  ggpubr::stat_cor() +
  theme_custom()

It seems that there are a few outliers at the upper end of the distribution. Let’s see whether the correlation holds when they are removed.

The correlation is still quite high (\(R = 0.38\)) and highly significant, when we remove the MPs with the top 20 highest in- and out-degrees.

top_20_in <- retweet_network %>% 
  mutate(in_degree = centrality_degree(mode = "in")) %>% 
  arrange(desc(in_degree)) %>% 
  as_tibble() %>% 
  top_n(20) %>% 
  pull(name)

top_20_out <- retweet_network %>% 
  mutate(out_degree = centrality_degree(mode = "out")) %>% 
  arrange(desc(out_degree)) %>% 
  as_tibble() %>% 
  top_n(20) %>% 
  pull(name)

# %N>% activate nodes (tidygraph syntax)
retweet_network %N>%
  mutate(
    in_degree = centrality_degree(mode = "in"),
    out_degree = centrality_degree(mode = "out")
  ) %>% 
  select(name, in_degree, out_degree) %>% 
  as_tibble() %>% 
  left_join(party_user, by = c("name" = "tw_id")) %>%
  filter(!name %in% top_20_in, !name %in% top_20_out) %>% 
  ggplot(aes(out_degree, in_degree)) + 
  geom_point(alpha = 0.5) +
  ggpubr::stat_cor() +
  theme_custom()

retweet_network %N>%
  filter(!party %in% c("Fraktionslos", NA)) %>% 
  mutate(
    in_degree = centrality_degree(mode = "in"),
    out_degree = centrality_degree(mode = "out")
  ) %>% 
  select(name, in_degree, out_degree) %>% 
  as_tibble() %>% 
  left_join(party_user, by = c("name" = "tw_id")) %>%
  filter(!name %in% top_20_in, !name %in% top_20_out) %>% 
  ggplot(aes(out_degree, in_degree)) + 
  geom_point(alpha = 0.5, aes(color = party)) +
  ggpubr::stat_cor(show.legend = F) +
  facet_wrap(~ party) +
  scale_color_manual(values = party_colors) +
  theme_custom()

Distance / Shortest Path Length

Most of the users are connected across (a path length of) 2 or 3 retweets.

distances_retweet_network <- retweet_network %>% 
  distances() %>%
  table() %>% 
  as_tibble() %>% 
  rename(Distance = 1) 

distances_retweet_network %>% 
  knitr::kable()

Distance	n
0	487
1	18554
2	105456
3	101042
4	11290
5	332
6	8

distances_retweet_network %>%
  ggplot(aes(x = Distance, y = n)) + geom_col() +
  ggtitle("Distribution of Distance (Shortest Path)") +
  theme_custom()

Network Visualization

Create a new custom theme, based on ggraph::theme_graph().

theme_graph_custom <- function() {
  font <- "Corbel"
  # base theme
  theme_graph() %+replace%
    theme(
      text = element_text(family = font),
      legend.text = element_text(size = 9),
      legend.title = element_blank(),
      legend.box.background = element_rect(
        colour = "black",
        fill = "white",
        linetype = "solid"
      ),
      # faceting
      strip.background = element_blank(),
      strip.text = element_text(color = "black"),
    )
}

Standard Plot

Using ggraph auto/default options to plot all nodes and edges, we can see that there is a strong pattern of “intra-party retweeting”. Interestingly, while most parties members exhibits almost exclusively retweet within the party, there is a strong visual intersection of SPD and Left Party retweets. To a lesser extent, this also repeats for party of the Greens with the SPD

plot_all_auto <- retweet_network %>% 
  ggraph(layout = "auto") + 
  geom_edge_link(alpha = 0.015) +
  geom_node_point(aes(color = party))  +
  scale_color_manual(values = party_colors) + 
  theme_graph_custom() 

plot_all_auto

Circular

Now let’s visualize the network in a circular way, highlighting the retweet dynamics in a way that underlines the party structure of the dataset.

plot_all_circular <- retweet_network %>% 
  filter(!party %in% c("Fraktionslos", NA)) %>% 
  ggraph(layout = "linear", circular = TRUE) + 
  geom_edge_arc(aes(color = party_from), alpha = 0.015) +
  scale_edge_color_manual(values = party_colors) + 
  coord_fixed() +
  theme_graph_custom() +
  theme(legend.position = "none")

plot_all_circular

Further Centrality Measures

Closeness Centrality

The Closeness Centrality is defined as the inverse total distance of paths between a node and all other nodes in the network. Shorter paths to others are thus an indicator of node centrality.

closeness_retweet_network <- retweet_network %>% 
  mutate(Closeness = centrality_closeness())

The Closeness Centrality is not dominated by the AfD, which was exhibiting the MPs with the highest degrees. This finding underlines that centrality measures that go beyond the degree centrality play an important role when assessing the actual dynamics of the dataset.

closeness_retweet_network %>% 
  filter(Closeness != 1) %>% 
  arrange(desc(Closeness)) %>% 
  select(name, Closeness, party) %>% 
  as_tibble() %>% 
  head(20) %>% 
  knitr::kable()

name	Closeness	party
konstantinkuhle	0.0011416	FDP
karl_lauterbach	0.0010776	SPD
brihasselmann	0.0010672	Grüne
marcobuschmann	0.0010616	FDP
heikomaas	0.0010604	SPD
larsklingbeil	0.0010504	SPD
johannesvogel	0.0010194	FDP
rkiesewetter	0.0010194	CDU/CSU
renatekuenast	0.0010183	Grüne
thomasoppermann	0.0010183	SPD
mgrossebroemer	0.0010163	CDU/CSU
c_lindner	0.0010152	FDP
konstantinnotz	0.0010152	Grüne
schneidercar	0.0010121	SPD
katarinabarley	0.0010121	SPD
nouripour	0.0010101	Grüne
mastrackzi	0.0010091	FDP
sigmargabriel	0.0010091	SPD
lambsdorff	0.0010070	FDP
cem_oezdemir	0.0010040	Grüne

The closeness centrality seems to be actually normally distributed, while there are some outliers on the lower bound.

closeness_retweet_network %>% 
  filter(Closeness != 1) %>% 
  as_tibble() %>% 
  ggplot(aes(x = Closeness)) + 
  geom_density() +
  theme_custom()

While most parties have an approximately equally distributed Closeness Centrality, the AfD has the lowest one with a visually perceivable difference. For interpretational reasons, this is likelly due to internal heterogeneity, given the populist character and short existence period of the party.

closeness_retweet_network %>% 
  filter(!party %in% c("Fraktionslos", NA)) %>%
  filter(Closeness != 1) %>% 
  as_tibble() %>% 
  ggplot(aes(x = Closeness, y = reorder(party, Closeness), fill = party)) + 
  ggridges::stat_density_ridges(alpha = 0.5, quantile_lines = T, quantiles = 4) +
  scale_fill_manual(values = party_colors) +
  theme_custom() +
  labs(y = "")

Betweenness Centrality

TODO: explain betweenness centrality

betweenness_retweet_network <- retweet_network %>% 
  mutate(Betweenness = centrality_betweenness())

betweenness_retweet_network %>% 
  arrange(desc(Betweenness)) %>% 
  select(name, Betweenness, party) %>% 
  as_tibble() %>% 
  head(20) %>% 
  knitr::kable()

name	Betweenness	party
tschipanski	26989.810	CDU/CSU
s_muenzenmaier	19464.496	AfD
stbrandner	18131.114	AfD
olliluksic	14463.042	FDP
rkiesewetter	12089.569	CDU/CSU
_martinneumann	8452.709	FDP
fabiodemasi	8202.163	DIE LINKE.
joanacotar	8010.589	AfD
hubertus_heil	7998.812	SPD
c_lindner	7090.924	FDP
gtzfrmming	6647.937	AfD
timon_gremmels	6566.856	SPD
wanderwitz	6504.270	CDU/CSU
matthiashauer	6361.569	CDU/CSU
udohemmelgarn	6303.254	AfD
mgrossebroemer	6059.420	CDU/CSU
sven_kindler	5791.922	Grüne
larsklingbeil	5217.861	SPD
drandreasnick	4975.422	CDU/CSU
konstantinnotz	4778.380	Grüne

betweenness_retweet_network %>% 
  as_tibble() %>% 
  ggplot(aes(x = Betweenness)) + 
  geom_histogram() +
  theme_custom()

betweenness_retweet_network %>% 
  as_tibble() %>% 
  ggplot(aes(x = Betweenness)) + 
  geom_histogram() +
  scale_x_log10() +
  theme_custom()

betweenness_retweet_network %>% 
  filter(!party %in% c("Fraktionslos", NA)) %>%
  filter(Betweenness != 1) %>% 
  as_tibble() %>% 
  ggplot(aes(x = Betweenness, color = party)) + 
  facet_wrap(~ party) +
  geom_density() +
  scale_x_log10() +
  # geom_density() +<
  scale_color_manual(values = party_colors) +
  theme_custom() +
  labs(y = "")

Selecting Subgraph for Visualization

Selecting a subgraph (sub-network) based on the largest component.

Community Detection

Since we are dealing with a directed network, not every type of community detection algorithm is appropriate.

retweet_network_group_betweenness <- retweet_network %>% 
  mutate(group = group_edge_betweenness(n_groups = 6))

Analyzing Retweet Dynamics of German MPs in the 19th Legislative Period

Load and Manipulate Dataset

Create `data.frame` that exhibits information of referenced `Tweets`

Get author information of `Tweets` that were referenced

Create final `data.frame` that only consists of `retweets` that occured within the dataset

Validate Retweet-Network Structure

Plot most `retweeted` MPs

Plot Top 20

Plot Top 20 by Party

Construct Retweet-Network

Degree Centrality

Out-degree

In-degree

Comparing Out- and In-degree

Distance / Shortest Path Length

Network Visualization

Standard Plot

Circular

Further Centrality Measures

Closeness Centrality

Betweenness Centrality

Selecting Subgraph for Visualization

Community Detection

Community Detection in Relationship with Party Affiliation

Further Work

Load and Manipulate Dataset

Create data.frame that exhibits information of referenced Tweets

Get author information of Tweets that were referenced

Create final data.frame that only consists of retweets that occured within the dataset

Validate Retweet-Network Structure

Plot most retweeted MPs

Plot Top 20

Plot Top 20 by Party

Construct Retweet-Network

Degree Centrality

Out-degree

In-degree

Comparing Out- and In-degree

Distance / Shortest Path Length

Network Visualization

Standard Plot

Circular

Further Centrality Measures

Closeness Centrality

Betweenness Centrality

Selecting Subgraph for Visualization

Community Detection

Community Detection in Relationship with Party Affiliation

Further Work

Relationship of Centrality and Social Media Profile Influence

Create `data.frame` that exhibits information of referenced `Tweets`

Get author information of `Tweets` that were referenced

Create final `data.frame` that only consists of `retweets` that occured within the dataset

Plot most `retweeted` MPs